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Both the unpredictable nature of the game and the wide range of performance among players pose difficulties in selecting a cricket squad. When selecting players, it is important to consider their previous track record on the field. However, comparing performance indicators from previous games with those that are about to take place is far from a realistic approach. It was human opinion-live commentary (i.e., expert opinion)-that enabled us to make this a reality. Commentary is the best source of actual thoughts from veteran players at the time of any event because it is provided in real time. As a result, any knowledge gained from the commentary is beneficial to any player's overall performance metric. During our research, we established a framework for collecting actual commentary and analyzing it to determine performance indicators. For demonstrating the successful proposals from our framework, we have conducted many variants of testing. In our trial review, we discovered that our system could collect commentary and recommend the most likely best players for any forthcoming match with high efficiency.

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The most prominent form of human communication and interaction is speech. It plays an indispensable role for expressing emotions, motivating, guiding, and cheering. An ill-intentioned speech can mislead people, societies, and even a nation. A misguided speech can trigger social controversy and can result in violent activities. Every day, there are a lot of speeches being delivered around the world, which are quite impractical to inspect manually. In order to prevent any vicious action resulting from any misguided speech, the development of an automatic system that can efficiently detect suspicious speech has become imperative. In this study, we have presented a framework for acquisition of speech along with the location of the speaker, converting the speeches into texts and, finally, we have proposed a system based on long short-term memory (LSTM) which is a variant of recurrent neural network (RNN) to classify speeches into suspicious and nonsuspicious. We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model. A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system. The experimental results show that our proposed deep learning-based model provides the highest accuracy compared to other algorithms.

Recent advancements in high-speed communications and high-capacity computing systems have contributed to major growth in the data volume of databases. Data mining is a crucial part of information retrieval; it is often termed as database knowledge discovery. It consists of techniques for examining massive data sets, to find hidden (but possibly important) information. Three interesting fields in data mining are affinity analysis, bibliomining, and technology mining. Affinity analysis provides data mining techniques to determine the similarity among objects; bibliomining is a combination of data mining, bibliometrics, and data warehousing; technology mining is a research topic that is an obstacle to many scientists in the fields of time association, enterprise association, and computer programming. We present a systematic review of the notable research articles in the fields of affinity analysis, bibliomining, and technology mining published between 2000 and December 2021. We provide a systematic analysis of the selected literature by specifying the major contributions, used data sets, performance evaluations, and limitations. Our findings demonstrate that affinity analysis interoperability extends well beyond market basket analysis. We also demonstrate that, in the age of big data, the personalized needs of users are the driving forces behind the evolution of the digital library from a resource-sharing service to a user-centered service. Finally, this article provides insight into major advances and outstanding challenges in the fields of affinity analysis, bibliomining, and technology mining.